Watershed classification by remote sensing indices: A fuzzy c-means clustering approach

Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean (FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups. The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently, the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures.     

Applying wavelet transformation and artificial neural networks to develop forecasting-based reservoir operating rule curves

ABSTRACT

In order to provide more accurate reservoir-operating policies, this study attempts to implement effective monthly forecasting models. Seven inflow forecasting schemes, applying discrete wavelet transformation and artificial neural networks are proposed and provided to forecast the monthly inflow of Dez Reservoir. Based on some different performance indicators the best scheme is achieved comparing to the observed data. The best forecasting model is coupled with a simulation-optimization framework, in which the performance of five different reservoir rule curves can be compared. Three applied rules are based on conventional Standard operation policy, Regression rules, and Hedging rule, and two others are forecasting-based regression and hedging rules. The results indicate that forecasting-based operating rule curves are superior to the conventional rules if the forecasting scheme provides results accurately. Moreover, it can be concluded that the time series decomposition of the observed data enhances the accuracy of the forecasting results efficiently.     

Satellite gradiometry using a satellite pair

The GRACE mission has substantiated the low–low satellite-to-satellite tracking (LL-SST) concept. The LL-SST configuration can be combined with the previously realized high–low SST concept in the CHAMP mission to provide a much higher accuracy. The line of sight (LOS) acceleration difference between the GRACE satellite pair, the simplest form of the combined observable, is mostly used for mapping the global gravity field of the Earth in terms of spherical harmonic coefficients. As an alternative observable, a linear combination of the gravitational gradient tensor components is proposed. Being a one-point function and having a direct relation with the field geometry (curvature of the field at the point) are two noteworthy achievements of the alternative formulation. In addition, using an observation quantity that is related to the second-instead of the first-order derivatives of the gravitational potential amplifies the high-frequency part of the signal. Since the transition from the first- to the second-order derivatives includes the application of a finite-differences scheme, the high-frequency part of the noise is also amplified. Nevertheless, due to the different spectral behaviour of signal and noise, in the end the second-order approach leads to improved gravitational field resolution. Mathematical formulae for the gradiometry approach, for both linear and higher-degree approximations, are derived. The proposed approach is implemented for recovery of the global gravitational field and the results are compared with those of LOS acceleration differences. Moreover, LOS acceleration difference residuals are calculated, which are at the level of a few tenths of mGal. Error analysis shows that the residuals of the estimated degree variances are less than 10^–3. Furthermore, the gravity anomaly residuals are less than 2 mGal for most points on the Earth.     

Evaluating the compatibility of multi-functional and intensive urban land uses

This research is aimed at developing a model for assessing land use compatibility in densely built-up urban areas. In this process, a new model was developed through the combination of a suite of existing methods and tools: geographical information system, Delphi methods and spatial decision support tools: namely multi-criteria evaluation analysis, analytical hierarchy process and ordered weighted average method. The developed model has the potential to calculate land use compatibility in both horizontal and vertical directions. Furthermore, the compatibility between the use of each floor in a building and its neighboring land uses can be evaluated. The method was tested in a built-up urban area located in Tehran, the capital city of Iran. The results show that the model is robust in clarifying different levels of physical compatibility between neighboring land uses. This paper describes the various steps and processes of developing the proposed land use compatibility evaluation model (CEM).     

Effect of drought and salinity stresses on morphological and physiological characteristics of canola

Environmental stresses such as salinity and drought are the most important factors affecting yield reduction and crops productivity. In order to investigate the effects of drought and salinity stresses on morphological and physiological characteristics of canola, an experimental campaign was conducted as a split plot based on a randomized complete block design with three replications at greenhouse of Agriculture Research Center and Natural Resources of East Azarbaijan in 2014. Main plots were including drought stress at four levels (0, ?4, ?8 and ?12 bar polyethylene glycol solution) and subplots including salinity stress at four levels of sodium chloride (0, 75, 150 and 225 mM). The results analysis of variance indicated that the interaction of drought and salinity stresses was significant on leaf area and relative water content of leaf. The most leaf area (383.03 cm^?2) was obtained at non-stress treatments. The results showed that drought stress conditions led to significant reduction in relative water content in leaf. The highest proline (0.08 µm/g fresh weight) and soluble sugars (0.12 mg/g fresh weight) contents were observed at treatments of ?12 bar polyethylene glycol. Also, the least proline (0.04 µm/g fresh weight) and soluble sugar (0.06 mg/g fresh weight) contents were achieved at treatments of drought non-stress.     

Prediction of TBM Penetration Rate Using Fuzzy Logic,Particle Swarm Optimization and Harmony Search Algorithm

Geotechnical and Geological Engineering - Tunnel Boring Machine (TBM) penetration rate prediction is one of the most important problem in tunneling projects. Estimating of Tunnel Boring Machine...     

Cycle slip detection and repair of undifferenced single-frequency GPS carrier phase observations

Cycle slip detection and repair is an important issue in the GPS data processing. Different methods have been developed to detect and repair cycle slips on undifferenced , single- or double-differenced observations. The issue is still crucial for high-precision GPS positioning, especially for the undifferenced GPS observations. A method is proposed to fix cycle slips based on the generalized likelihood ratio (GLR) test. The method has a good performance on cycle slip fixing of undifferenced carrier phase observations on individual frequencies, either on L1 or on L2, without making a linear combination among the observables. The functional model is a piecewise cubic curve fitted to a number of consecutive data using the least squares cubic spline approximation (LS-CSA). For fixing the cycle slips, an integer estimation technique is employed to determine the integer values from the float solution. The performance of the proposed method is then compared with the existing two methods using simulated data. The results on a few GPS data sets with sampling rate of 1 Hz or higher confirm that this method can detect and correct all simulated cycle slips regardless of the size of the cycle slip or the satellite elevation angle. The efficacy of the method is then investigated on the GPS data sets with lower sampling rates of 5, 10, and 30 s. The results indicate that the proposed method always performs the best for the data sets considered. This is thus an appropriate method for cycle slip detection and repair of single-frequency GPS observations.     

Late glacial and early Holocene hydroclimate variability in northwest Iran (Talesh Mountains) inferred from chironomid and pollen analysis

We reconstructed the paleohydrologic and climatic history of the Lake Neor region, NW Iran, from the end of the late glacial to the middle Holocene (15,500–7500 cal yr BP). Subfossil chironomid and pollen assemblages in a sediment core from a peatland located south of Lake Neor enabled identification of four main hydrologic phases. The period 15,500–12,700 cal yr BP was characterized by a relatively dry climate with an open landscape, suggested by the abundance of Irano-Turanian steppe plants (e.g. Amaranthaceae, Artemisia and Cousinia). Dominance of several shallow-water and semi-terrestrial chironomid taxa (e.g. Pseudosmittia, Smittia/Parasmittia and Paraphaenocladius/Parametriocnemus) during this period is indicative of lower water tables in the wetland. Between 12,700 and 11,300 cal yr BP, chironomid taxa indicate higher wetland water tables, as suggested by the presence of Zavrelia, Chironomus anthracinus/plumosus-type and Micropsectra, which are inhabitants of open-water, lacustrine areas. The open-steppe vegetation remained dominant in the watershed during this time. Increasing wetland moisture could be explained by: (1) cool summers that reduced the evaporation rate; and/or (2) a decrease in duration of the summer dry season. The period 11,300–8700 cal yr BP was characterized by lower wetland moisture, contemporaneous with a delay in the expansion of deciduous forest, suggesting persistent dry climate conditions throughout the beginning of the Holocene, which may have been related to the intensified seasonality of precipitation. Around 8700 cal yr BP, higher wetland water levels, inferred from chironomids, occurred simultaneously with the onset of regional deciduous forest expansion, probably caused by a shortening of the summer dry period. We concluded that chironomids are appropriate paleoecological proxies to investigate global and local hydrologic variability in the Middle East.     

Specification of a Coupled Geological and Wellbore Fluid Dynamics Model for Uncertainty Analysis and Risk-Based Design on a Subsea Oil Project

This paper describes the method and results of a probabilistic risk analysis that was used to provide a quantitative basis for a complex and high-stakes design decision for a subsea oil project. Geologic properties were specified as probability density functions based on information from a small number of exploration and appraisal wells. The geological information was coupled to a wellbore fluid dynamics model to simulate operational outcomes for potential well kill operations. Conventional probabilistic sampling and a more efficient experimental design approach were both employed. The key results are cumulative density functions for critical bases of design that are used for risk analysis and final equipment selection. The analysis reinforces the importance of interdisciplinary collaboration, in this case the subsurface disciplines and well engineering, to achieve efficient risk-based designs.     

Multi-time-scale analysis of hydrological drought forecasting using support vector regression (SVR) and artificial neural networks (ANN)

In this study, the preprocessing of the gamma test was used to select the appropriate input combination into two models including the support vector regression (SVR) model and artificial neural networks (ANNs) to predict the stream flow drought index (SDI) of different timescales (i.e., 3, 6, 9, 12, and 24 months) in Latian watershed, Iran, which is one of the most important sources of water for the large metropolitan Tehran. The variables used included SDI _t , SDI _t ? 1, SDI _t ? 2, SDI _t ? 3, and SDI _t ? 4 monthly delays. Two variables including SDI _t and SDI _t ? 1 with lower gamma values were identified as the most optimal combination of variables in all drought timescales. The results showed that the gamma test was able to correctly identify the right combination for the forecasting of 6, 9, and 12 months SDI using the ANN model. Also, the gamma test was considered in selecting the appropriate inputs for identifying the values of 9, 12, and 24 months SDI in SVR. The support vector machine approach showed a better efficiency in the forecast of long-term droughts compared to the artificial neural network. In total, among forecasts made for 30 scenarios, the support vector machine model only in scenario 3 of SDI₃, scenario 1 of SDI₆, and scenarios 2 and 3 of SDI₂₄ represented poorer efficiency compared to the artificial neural network (MLP layer), but in other scenarios, the results of SVR had better efficiency.